228 citations · 813 across the 67 of their papers we have counts for
6 papers · 2 filters
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights
Lingxiao Zhao, Leman Akoglu
It is common practice of the outlier mining community to repurpose classification datasets toward evaluating various detection models. To that end, often a binary classification da…
FairOD: Fairness-aware Outlier Detection
Shubhranshu Shekhar, Neil Shah, Leman Akoglu
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population. However, when being a minority (as de…
Automating Outlier Detection via Meta-Learning
Yue Zhao, Ryan A. Rossi, Leman Akoglu
Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a…
Connecting Graph Convolutional Networks and Graph-Regularized PCA
Lingxiao Zhao, Leman Akoglu
Graph convolution operator of the GCN model is originally motivated from a localized first-order approximation of spectral graph convolutions. This work stands on a different view;…
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao +3
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connect…
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Yue Zhao, Xiyang Hu, Cheng Cheng +11
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and…